The Core Building Blocks of LangChain - Middleware for the LLM Stack

This guide is especially useful for developers working at the intersection of AI, biotech, and bioinformatics - where large language models (LLMs) meet genomic data, protein structures, and sequence analysis.

Large Language Models are powerful, but raw. They don’t know your business context, don’t remember past conversations, and can’t take actions by themselves.

LangChain fixes that.

It’s not just a library. It’s the glue between LLMs and everything else.

This article is for developers who want to understand the architecture of LangChain. Whether you’re building an LLM app or researching how to plug models into real-world data and workflows, this guide breaks down the six core building blocks LangChain is built on.

1. Model I/O

LLMs don’t speak plain JSON or Markdown.

LangChain abstracts the messy formatting and parsing. You write prompts using templates, get structured outputs back, and even support streaming.

You’ll use this when:

• Building dynamic prompts from user inputs
• Streaming long responses
• Switching between OpenAI, Hugging Face, and others

2. Memory

By default, LLMs are stateless. They forget everything between prompts.

LangChain adds memory. You can store prior messages, summaries, or even vector-based context.

You’ll use this when:

• Building a chatbot
• Creating a stateful assistant
• Caching conversation history for smoother UX

3. Retrieval

Retrieval brings facts to LLMs - such as protein sequences or CRISPR guides from genomic databases.

You embed documents and store them in a vector database. Then LangChain retrieves the most relevant ones before each prompt.

You’ll use this when:

• Connecting your app to docs, PDFs, Notion, or SQL
• Answering questions with citations
• Doing RAG (Retrieval-Augmented Generation)

4. Embeddings

Embeddings are how text becomes searchable.

LangChain lets you plug in any embedding model to convert text into vectors for similarity search.

You’ll use this when:

• Indexing docs for RAG
• Comparing user queries to content
• Storing knowledge in vector space

5. Agents

Agents are the brains that decide what to do.

They choose tools, call APIs, and chain together reasoning steps.

You’ll use this when:

• Giving LLMs access to tools (like calculators, APIs, Python)
• Building autonomous agents
• Letting your app decide what to do next

6. External Data

LLMs need context. Sometimes, that means real data.

LangChain lets you fetch live information from APIs, databases, and more.

You’ll use this when:

• Pulling data from Postgres, MongoDB, Airtable, REST APIs
• Giving your bot access to internal systems
• Combining knowledge with dynamic inputs

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Putting it all together

Here’s a simplified flow:

User
↓
Application
↓
LangChain Middleware
├── 🧾 Prompt Template (Model I/O)
├── 🧠 Check Memory (Memory)
├── 📚 Retrieve Docs (Retrieval)
├── 🧲 Embed Query (Embeddings)
├── 🌐 Fetch External Data (APIs, Databases)
├── 🤖 Decide Next Step (Agents)
└── 🎯 Generate Model Output (Model I/O)
↓
Application
↓
User

LangChain gives you Lego blocks. You choose how to snap them together.

Some apps use just 2 or 3 components. Others use the full stack.

But if you’re building anything LLM-powered - from search to chatbots to agents - LangChain is the infrastructure layer that makes your application smart, connected, and production-ready.

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Follow my journey at bioaisoftware.engineer as I explore more tools like this while building my dream role: BioAI Software Engineer.